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I know of no open-source (software) tools dedicated to sentiment analysis. Instead, a variety of open-source text-analytics tools — natural-language processing for information extraction and classification — can be applied for sentiment analysis. They include —

I bet someone’s doing sentiment with the Stanford NLP tools, http://www-nlp.stanford.edu/software/, although my understanding is the maximum-entropy classification isn’t the best approach for sentiment. I’m no scientist so I won’t go into this.

Powerful, I can’t say. Where machine learning is involved, a lot will depend on your training set.

Note that the tools above work on textual sources. There may be open-source tools out there for information extraction from non-textual, sentiment-bearing sources such as speech (with the outputs fed into a classification engine such as some fo the above), but I haven’t looked into them. If you know of any, or have additions for my list above, please send me a note (grimes(at)altaplana.com).

Thanks for the nice summary, Seth! The Apache Mahout library includes a Latent Dirichlet Allocation algorithm that can be used for topic identification, and might be used as part of a sentiment analysis process.

Hi Karimkhanp maybe it’s too late to answer your question but it will be usefull for others who asks the same question; Actually there is some tools, I know two of them and tested only one, there is Tashaphyne, and ISRI python packages you can find documentation for the two packages.